To tackle the deep sensor fusion problem, Chen et al. [1]} proposed selective sensor fusion, a framework that selectively learns context-dependent representations for visual inertial pose estimation.
Their intuition is that the importance of features from different modalities should be considered according to the exterior (i.e., environmental) and interior (i.e., device/sensor) dynamics, by fully exploiting the complementary behaviors of two sensors.
Their approach outperforms those without a fusion strategy, e.g. VINet, avoiding catastrophic failures.
